Academic Journal
Evolutionary Algorithm Approaches for Cherry Fruit Classification Based on Pomological Features.
| Τίτλος: | Evolutionary Algorithm Approaches for Cherry Fruit Classification Based on Pomological Features. |
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| Συγγραφείς: | Akyol, Erhan, Alatas, Bilal, Ozgen, Inanc |
| Πηγή: | Agriculture; Basel; Nov2025, Vol. 15 Issue 21, p2207, 22p |
| Θεματικοί όροι: | Evolutionary algorithms, Pest control, Artificial intelligence, Agricultural industries, Integrated pest control, Fruit quality, Data mining |
| Περίληψη: | The cherry fruit fly (Rhagoletis cerasi L.) poses a major threat to global cherry production, with significant economic implications. This study presents an innovative approach to assist pest control strategies by classifying cherry fruit samples based on pomological data using evolutionary rule-based classification algorithms. A unique dataset comprising 396 samples from five different coloring periods was collected, focusing particularly on the second pomological period when pest activity peaks. Three evolutionary algorithms, CORE (Evolutionary Rule Extractor for Classification), DMEL (Data Mining with Evolutionary Learning for Classification) and OCEC (Organizational Evolutionary Classification), were applied to find interpretable classification rules that find whether an incoming cherry sample belongs to the second pomological period or other periods. Two distinct fitness functions were used to evaluate the algorithms' performance. The results of the algorithms are compared with various visual graphs and the metric values are compared with visual graphs in a similar fashion. The findings highlight the potential of explainable AI models in enhancing agricultural decision-making and offer a novel, data-based methodology for integrated pest management in cherry production for the prediction of cherry fruit phenology class. [ABSTRACT FROM AUTHOR] |
| Copyright of Agriculture; Basel is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20770472&ISBN=&volume=15&issue=21&date=20251101&spage=2207&pages=2207-2228&title=Agriculture; Basel&atitle=Evolutionary%20Algorithm%20Approaches%20for%20Cherry%20Fruit%20Classification%20Based%20on%20Pomological%20Features.&aulast=Akyol%2C%20Erhan&id=DOI:10.3390/agriculture15212207 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Evolutionary Algorithm Approaches for Cherry Fruit Classification Based on Pomological Features. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akyol%2C+Erhan%22">Akyol, Erhan</searchLink><br /><searchLink fieldCode="AR" term="%22Alatas%2C+Bilal%22">Alatas, Bilal</searchLink><br /><searchLink fieldCode="AR" term="%22Ozgen%2C+Inanc%22">Ozgen, Inanc</searchLink> – Name: TitleSource Label: Source Group: Src Data: Agriculture; Basel; Nov2025, Vol. 15 Issue 21, p2207, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pest+control%22">Pest control</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+industries%22">Agricultural industries</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+pest+control%22">Integrated pest control</searchLink><br /><searchLink fieldCode="DE" term="%22Fruit+quality%22">Fruit quality</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The cherry fruit fly (Rhagoletis cerasi L.) poses a major threat to global cherry production, with significant economic implications. This study presents an innovative approach to assist pest control strategies by classifying cherry fruit samples based on pomological data using evolutionary rule-based classification algorithms. A unique dataset comprising 396 samples from five different coloring periods was collected, focusing particularly on the second pomological period when pest activity peaks. Three evolutionary algorithms, CORE (Evolutionary Rule Extractor for Classification), DMEL (Data Mining with Evolutionary Learning for Classification) and OCEC (Organizational Evolutionary Classification), were applied to find interpretable classification rules that find whether an incoming cherry sample belongs to the second pomological period or other periods. Two distinct fitness functions were used to evaluate the algorithms' performance. The results of the algorithms are compared with various visual graphs and the metric values are compared with visual graphs in a similar fashion. The findings highlight the potential of explainable AI models in enhancing agricultural decision-making and offer a novel, data-based methodology for integrated pest management in cherry production for the prediction of cherry fruit phenology class. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Agriculture; Basel is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/agriculture15212207 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 2207 Subjects: – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Pest control Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Agricultural industries Type: general – SubjectFull: Integrated pest control Type: general – SubjectFull: Fruit quality Type: general – SubjectFull: Data mining Type: general Titles: – TitleFull: Evolutionary Algorithm Approaches for Cherry Fruit Classification Based on Pomological Features. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akyol, Erhan – PersonEntity: Name: NameFull: Alatas, Bilal – PersonEntity: Name: NameFull: Ozgen, Inanc IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20770472 Numbering: – Type: volume Value: 15 – Type: issue Value: 21 Titles: – TitleFull: Agriculture; Basel Type: main |
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